SMART-SLE: serology monitoring and repeat testing in systemic lupus erythematosus—an analysis of anti-double-stranded DNA monitoring

Abstract Objective Disease activity monitoring in SLE includes serial measurement of anti-double stranded-DNA (dsDNA) antibodies, but in patients who are persistently anti-dsDNA positive, the utility of repeated measurement is unclear. We investigated the usefulness of serial anti-dsDNA testing in predicting flare in SLE patients who are persistently anti-dsDNA positive. Methods Data were analysed from patients in a multinational longitudinal cohort with known anti-dsDNA results from 2013 to 2021. Patients were categorized based on their anti-dsDNA results as persistently negative, fluctuating or persistently positive. Cox regression models were used to examine longitudinal associations of anti-dsDNA results with flare. Results Data from 37 582 visits of 3484 patients were analysed. Of the patients 1029 (29.5%) had persistently positive anti-dsDNA and 1195 (34.3%) had fluctuating results. Anti-dsDNA expressed as a ratio to the normal cut-off was associated with the risk of subsequent flare, including in the persistently positive cohort (adjusted hazard ratio [HR] 1.56; 95% CI: 1.30, 1.87; P < 0.001) and fluctuating cohort (adjusted HR 1.46; 95% CI: 1.28, 1.66), both for a ratio >3. Both increases and decreases in anti-dsDNA more than 2-fold compared with the previous visit were associated with increased risk of flare in the fluctuating cohort (adjusted HR 1.33; 95% CI: 1.08, 1.65; P = 0.008) and the persistently positive cohort (adjusted HR 1.36; 95% CI: 1.08, 1.71; P = 0.009). Conclusion Absolute value and change in anti-dsDNA titres predict flares, including in persistently anti-dsDNA positive patients. This indicates that repeat monitoring of dsDNA has value in routine testing.


Introduction
SLE is a chronic autoimmune multisystem condition with a preponderance for affecting women of childbearing age.Higher disease activity, glucocorticoid exposure and increased numbers of flares have each been demonstrated to increase the risk of irreversible organ damage [1][2][3][4].Therefore, SLE patients require long term monitoring of disease activity in order to reduce the development of damage [5,6].Disease activity monitoring has been difficult to standardize given the heterogeneous nature of SLE; some patients have only mild manifestations with no evidence of damage, whereas others have severe disease with end stage organ damage [7,8].Monitoring patients using tools that predict flare is potentially of value to identify addressable risk factors for poor outcomes.
Several studies have sought to determine whether biomarkers can predict flare in SLE [9][10][11][12][13], including titres of autoantibodies to double stranded DNA (anti-dsDNA) [12,14].However, studies of flare prediction lack standardized methodology, and analyses of cross-sectional data have limited ability to guide strategies in the clinic [15].Furthermore, some patients are persistently serologically active, and the utility of repeating anti-dsDNA tests in this context is poorly understood, while a subgroup of SLE patients are clinically quiescent despite being serologically active, and fluctuations in anti-dsDNA and complement do not appear to predict flare in these patients [16,17].Lastly, providers use different assays to measure anti-dsDNA, thus encumbering standardization, monitoring and cross-referencing [18].
Despite these limitations, anti-dsDNA measurement is often performed repeatedly during SLE management.Widely used disease activity indices such as the SLE disease activity index (SLEDAI) require anti-dsDNA antibody test results to assign a score [19].Attainment of the lupus low disease activity state (LLDAS) has been demonstrated to reduce damage accrual in SLE [20][21][22][23], but SLEDAI is included in the definition of LLDAS, meaning serological testing is required to assess LLDAS attainment.As new treatments for SLE emerge, and treatment targets become part of routine practice, SLEDAI or other formal disease activity measurements may be required as part of reimbursed drug access, meaning the potential for a surge in the serial use of these tests, despite limited understanding about cost-effectiveness.We have recently reported very low utility of repeat testing for other common autoantibodies, anti-nuclear antibodies and extractable nuclear antibodies, albeit in the setting of diagnostic screening rather than monitoring of established disease [24,25].Should anti-dsDNA antibodies be serially tested in patients in whom the result is always positive?
In this study, we investigated the utility of serial anti-dsDNA testing in predicting the risk of flare in SLE patients.The focus was on those patients who were persistently anti-dsDNA positive, in order to determine whether the need for serial testing could be obviated in this group.

Study design, setting and participants
Data from the Asia Pacific Lupus Collaboration (APLC) cohort were used to conduct this study [26].This is a multi-country longitudinal registry of patient data collected prospectively across 25 sites in 13 countries.All patients met either the 1997 ACR Modified Classification Criteria [27] or the 2012 SLICC Classification Criteria [28] for SLE and provided written consent to be part of the APLC cohort.Each site obtained local ethics approval to participate in the APLC research activities.Storage of the central dataset and analyses of the pooled data have been approved by the Monash University Human Research Ethics Committee (MUHREC Project ID 18778).

Data collection
Data were collected during routine visits for patient care via electronic point of care data entry or paper or electronic forms from 2013 until 2021.Baseline data collected included age, duration of SLE, sex, ethnicity, baseline serology and comorbidities.At each visit SLEDAI-2K [29], physician global assessment (PGA, measured 0-3) [30], medication information and pathology results including serology were collected.Only visits where an anti-dsDNA result was available were included in analysis, and only patients who had at least two such visits were included.Flares were routinely captured at each visit using the Safety of Estrogens in Lupus Erythematosus National Assessment-SLEDAI flare index (SFI) [30,31].This captures flare using a composite of the following: (i) increases of at least 3 points in the SELENA-

Rheumatology key messages
• Anti-dsDNA results can fluctuate, but are persistently positive in nearly one-third of SLE patients.
• Anti-dsDNA results predict flares, including in patients who are persistently positive.
• Larger fluctuations in anti-dsDNA are more predictive of flares.

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Ai Li Yeo et al.
SLEDAI disease activity instrument, (ii) other new or worsening activity, medication changes and hospitalization not been captured in the SELENA-SLEDAI, and (iii) an increase in the physician's global assessment (visual analogue scale) !1.0.The severity of flare was captured as previously defined by Petri et al. [31].Damage accrual was recorded at baseline and annually using the SLICC-ACR Damage Index (SDI) [32].
Patients were categorized based on their anti-dsDNA results into three cohorts: persistently negative (patients who never had anti-dsDNA values above upper limit of normal range), fluctuating (anti-dsDNA results varying between negative and positive) and persistently positive (patients who always had anti-dsDNA results above upper limit of normal range).

Standardising anti-dsDNA results
Data on the specific assay performed at each site and across time were not available.Due to the multiple sites and assays used to determine the anti-dsDNA result, we standardized results by calculating an anti-dsDNA ratio, dividing the anti-dsDNA result by the upper limit of normal for that test.This ratio was then used to determine the relative change in anti-dsDNA result between tests, recorded as a vector (value and direction of change) rather than absolute value (Supplementary Fig. S1, available at Rheumatology online).

Statistical analysis
Statistical analysis was performed using Stata Version 15 (StataCorp, College Station, TX, USA).Continuous variables are described as either mean (standard deviation [SD]) or median (interquartile range [IQR]) depending on data distribution.Medians were compared using the Kruskal-Wallis (comparison of medians) test among anti-dsDNA persistently negative, fluctuating and persistently positive groups.Categorical variables are described as frequency and percentage and compared using the chi-square test.
Cox regression models were used to examine longitudinal associations of anti-dsDNA results and other factors associated with flare (mild-moderate or severe) at the subsequent visit in the persistently positive and fluctuating cohorts.For analysis of change in anti-dsDNA, the anti-dsDNA ratios calculated at two consecutive visits were used to assess prediction of flare assessed at a third consecutive visit (Supplementary Fig. S1, available at Rheumatology online).Each univariable Cox regression model contains one dependent (or exposure) variable only.If dependent variables demonstrated univariable associations with P-values <0.1, they were included in multivariable regression models.Prentice, Williams and Peterson modelling with gap time (PWP-GT) was incorporated when setting up data to allow multiple 'failures', i.e. flare recurrence.In addition, clustering was specified in these Cox regression models to account for intragroup correlation.The results from the time-dependent Cox regression analyses are presented as hazard ratios with corresponding 95% CI.A P-value 0.05 was considered statistically significant.
Three patterns of anti-dsDNA results over time were observed, i.e. persistently negative (1260 patients [36%]), fluctuating between negative and positive (1195 [34%]) and  1).The persistently negative cohort had less medication use during the observation period compared with the fluctuating and persistently positive anti-dsDNA cohort (  3).
In univariable Cox regression analyses, disease activity and laboratory values at each visit were analysed to determine whether they predicted flare at the subsequent visit.To determine the association of anti-dsDNA as a continuous variable with risk of flare, two approaches were used: absolute value of anti-dsDNA ratio, and change in anti-dsDNA ratio from the previous visit, recorded as amplitude and direction.
The results of the Cox regression analyses of associations with flare for the whole cohort, including patients who were persistently anti-dsDNA negative, are shown in Supplementary Tables S1 and S2, available at Rheumatology online.As the primary aim was to determine whether serial anti-dsDNA monitoring in patients who are persistently anti-dsDNA positive had utility, we focused on these patients.As an absolute value, anti-dsDNA ratio >3 was significantly associated with an increased risk of flare at the subsequent visit in persistently anti-dsDNA positive patients (unadjusted HR [95% CI] 1.64 [1.40, 1.91], P < 0.001) (Table 4).In the persistently anti-dsDNA positive cohort, change in anti-dsDNA ratio of at least 2-fold in a positive or negative direction had an unadjusted HR (95% CI) for flare of 1.35 (1.09, 1.68) (P ¼ 0.007) and 1.29 (1.05, 1.59) (P ¼ 0.014), respectively (Table 4).An increase or decrease of <2-fold did not predict flare.
In the persistently positive cohort, univariable analysis showed an increased risk of flares with activity in the following  19, 3.19]).Haematological activity at the previous visit was negatively associated in univariable analysis with subsequent flare (unadjusted HR [95% CI] 0.78 [0.62, 0.97]).Furthermore, in the persistently positive cohort we found that longer disease duration, older age and prednisolone dose >7.5 mg were associated with increased risk of flare.The following laboratory markers were also associated with an increased risk of flare in the persistently positive cohort: CRP >20 (unadjusted HR [95% CI] 2.67 In the fluctuating cohort, increased risk of flare at the subsequent visit was associated with activity in all domains of SLEDAI-2K except for fever, central nervous system and haematological (Table 4).Similarly, to the persistently positive cohort, elevated CRP (CRP >25, unadjusted HR [95% CI] 1.50 [1.08, 2.08]), ESR >25 (1.69 [1.54, 1.87]) and abnormal protein/creatinine ratio (1.92 [1.76, 2.10]) were associated with an increased risk of flare.Low C3 and C4 were associated with an increased risk of flare with unadjusted HR (95% CI) of 1.45 (1.35, 1.57) and 1.46 (1.33, 1.59) respectively.A normal lymphocyte count was found to be protective with lymphocyte counts <1 Â 10 9 /l having an unadjusted HR (95% CI) of 1.37 (1.26, 1.49).
We next adjusted for covariables that we considered important predictors of flare or that had demonstrated significance in univariable analysis.The final four models included the following parameters measured at the previous visit: lymphocyte count <1 Â 10 9 /l, prednisolone dose !7.5 mg, ESR, cutaneous, serositis and renal activity as measured on SLEDAI-2K.CRP was excluded from the final models due to collinearity with ESR, and the Pr/Cr ratio was excluded due to collinearity with renal activity.Haematological activity, vasculitis activity, central nervous system activity, and C3 and C4 were dropped from the final model as they became insignificant during the multivariable analysis.After multivariable adjustment, associations with flare were observed in persistently anti-dsDNA positive patients and fluctuating patients for both anti-dsDNA ratio as an absolute value and for fold change in anti-dsDNA between visits (Table 5).An absolute anti-dsDNA ratio >3 demonstrated a hazards ratio (95% CI) of 1.35 (1.17, 1.57) in the fluctuating cohort and 1.51 (1.26, 1.81) in the persistently positive cohort.In persistently anti-dsDNA positive patients, a change in anti-dsDNA ratio of at least 2-fold in a positive or negative direction at the prior visit had an adjusted HR (95% CI) for flare of 1.26 (1.00, 1.57) (P ¼ 0.050) and 1.36 (1.07, 1.71) (P ¼ 0.010), respectively.The fluctuating cohort had a HR (95% CI) of 1.45 (1.14, 1.84) for a 2-fold increase and a HR (95% CI) of 1.35 (1.07, 1.75) for a 2-fold decrease in the anti-dsDNA ratio.

SMART-SLE
One hundred and ninety-eight patients (5.7%), despite meeting SLE classification criteria, did not have a record of a positive ANA test result.Analysis after removal of these patients did not impact on the associations of anti-dsDNA with flare (Supplementary Table S3, available at Rheumatology online).

Discussion
Disease activity monitoring in SLE has long been challenging.This is in part due to the spectrum of disease manifestations that present in SLE and in part due to the challenges of finding biomarkers with which to monitor disease activity.In  [9][10][11]14].For example, Ho et al. described an increase in dsDNA prior to a flare with a subsequent decrease at the time of flare [33].Gladman et al. described a subset of SLE patients who have persistent serological activity but remain clinically quiescent [17], and we and others have shown that such patients are at higher risk of flare than serologically negative patients [34], but whether absolute levels or fluctuations in anti-dsDNA predict disease flare in patients who are persistently positive is poorly understood.Recent studies, albeit in the setting of screening rather than disease monitoring, have highlighted low utility of serial autoantibody testing [24,25], and the healthcare cost of ongoing testing in patients whose result does not change between positive and negative, a group comprising two-thirds of our cohort, is significant.In the current study, we examined whether quantification of anti-dsDNA could predict increased risk of flare for SLE patients in patients whose results were persistently positive, as well as in SLE patients in general.
The primary aim of this study was to assess the utility of serial dsDNA monitoring in established SLE patients whose results were persistently positive, to determine whether the need for serial testing could be obviated in this group.Patients who were persistently anti-dsDNA positive had a different disease phenotype compared with those who were persistently anti-dsDNA negative, with a shorter disease duration at enrolment, younger age of diagnosis and more musculoskeletal involvement at baseline, in keeping with previous literature [35,36].Patients who were persistently anti-dsDNA positive also had higher disease activity over time, including when anti-dsDNA was subtracted from the SLEDAI-2K measure and when disease activity was measured using a physician global assessment.Use of immunosuppressive medications and cumulative prednisolone exposure was lowest in the persistently anti-dsDNA negative cohort, and of note, the cumulative prednisolone dose, and likelihood of cyclophosphamide and mycophenolate use, was highest in the fluctuating cohort.It is not clear if the associations with fluctuating anti-dsDNA reflect the impact of these drugs on anti-dsDNA or disease characteristics intrinsic to these patients that led to these treatments being used.The fluctuating cohort was followed for a longer period of time, influencing cumulative prednisolone dose.Although biological use was infrequent in this cohort, the increased use of B cell-targeting therapies such as rituximab and belimumab in patients who have fluctuating or persistently positive anti-dsDNA reflects the subset of patients that were included in clinical trials [37,38].
To our knowledge, this is the first study to specifically address the utility of serial anti-dsDNA testing in a large cohort of patients where results are not varying between negative and positive.Our findings demonstrate utility in serial quantification of anti-dsDNA in patients who were persistently positive, by showing that absolute values and changes in anti-dsDNA were predictive of flare.After multivariable adjustment, patients who had an increase in anti-dsDNA of >2-fold had a 26% increased risk of flare.These results are in keeping with some previous studies that have shown an increase in anti-dsDNA can precede a flare, although this has not been demonstrated in all studies [9,12,[39][40][41][42].Interestingly, the risk of flare was also increased in patients whose anti-dsDNA reduced >2-fold, by 36%, suggesting either that it is the volatility of anti-dsDNA results between visits that is predictive, or potentially that different organ systems are affected differently in relation to anti-dsDNA but captured similarly in the flare instrument used.This was true for patients in the fluctuating cohort and the persistently positive cohort.Deposition of antibodies into specific organs is potentially another explanation for this phenomenon.One previous study has found that a reduction of anti-dsDNA around the time of increased disease activity occurred in a select few patients prior to the initiation of therapy rather than prior to disease flare [43].This observation was observed particularly for renal flares with the authors of this study hypothesizing that dsDNA deposition in the kidney was responsible for the reduction in titres.a Hazard ratio adjusted for ESR, prednisolone use of at least 7.5 mg at previous visit, lymphocyte count, evidence of renal, cutaneous or serositis disease activity at previous visit.Central nervous system activity, vasculitis activity, C4 and C3 were dropped from the final model as they became insignificant during multivariable analysis.CRP and Pr/Cr ratio were excluded in the final model due to collinearity with ESR and renal activity respectively.
b Time-dependent Cox proportional hazard ratios.

SMART-SLE
The adjusted hazards ratios between fluctuating and persistently positive groups were similar for both the absolute anti-dsDNA ratio and the change in anti-dsDNA ratio.This suggests that patients who have ever been anti-dsDNA positive represent a subset of patients distinct from those who have always been negative.More recently there has been work looking into inflammatory and regulatory cytokines and their ability to predict flare [44].Future studies of the association of anti-dsDNA with these cytokines would be of interest.
We also found that an increase in the absolute value of anti-dsDNA at a given time point was associated with the likelihood of flare at the subsequent visit.Patients who have positive anti-dsDNA at baseline have been shown to be more likely to flare [45,46].Our findings suggest a role for both the absolute value of anti-dsDNA and the variability of the result in predicting the risk of future flare.
There are several limitations to this study.First, due to the multisite nature of this cohort, there were multiple assays used to measure anti-dsDNA.To attempt to account for this, we analysed all anti-dsDNA results as a ratio to the upper limit of the normal range for each assay.This may not have captured variations in dynamic range between ELISA-based and other assay formats.Furthermore, availability of anti-dsDNA results may influence the treating physician's clinical assessment, including the PGA [47], and impact the scoring of flares.This study was performed in the Asia-Pacific region, which includes countries with variations in healthcare systems and access to therapies, and ideally findings would be confirmed in independent cohorts.
In conclusion, this study, performed in a large longitudinally followed multinational cohort, suggests that serial monitoring of anti-dsDNA in patients who are persistently anti-dsDNA positive is not redundant.Rather, absolute values and change between visits are useful in predicting flares.Patients who were persistently anti-dsDNA negative were clinically differentiated from persistently positive and fluctuating anti-dsDNA patients, suggesting a different disease trajectory in which other predictors of flare should be sought.Future research into the predictive value of fluctuations in anti-dsDNA in patients who are serologically active but clinically quiescent would be of value.

Table 2 .
Medication usage ever in SLE patients categorized as anti-dsDNA persistently negative, fluctuating or persistently positive Medication use was recorded if a medication was used at any time during observation.For categorical variables, the chi square test was used, for continuous variables, the Kruskal-Wallis test.IQR: interquartile range. a

Table 3 .
Disease activity and flare in SLE patients categorized as anti-dsDNA persistently negative, fluctuating or persistently positive Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) Damage Index; SLEDAI-2K: Systemic Lupus Erythematosus Disease Activity Index 2000.
a For categorical variables, the chi square test was used, for continuous variables, the Kruskal-Wallis test.b Flare rate was calculated by dividing the total number of flares by the observation period.IQR: interquartile range; PGA: physician global assessment; SDI:

Table 4 .
Unadjusted hazard ratio of associations with flare a Flare was captured using the Safety of Estrogens in Lupus Erythematosus National Assessment-SLEDAI flare index.b Time-dependent Cox proportional hazards ratios.c Anti-dsDNA ratio calculated by taking the result divided by the upper limit of normal for that assay.UPCR: urine protein/creatinine. a

Table 5 .
Adjusted hazard ratios of associations with flare (multivariable analysis)